Polynomial geometric programming as a special case of semi-infinite linear programming
Journal of Optimization Theory and Applications
An infeasible interior-point algorithm for solving primal and dual geometric programs
Mathematical Programming: Series A and B - Special issue: interior point methods in theory and practice
The Design of the XMP Linear Programming Library
ACM Transactions on Mathematical Software (TOMS)
Geometric programming with fuzzy parameters in engineering optimization
International Journal of Approximate Reasoning
Fuzzy measures for profit maximization with fuzzy parameters
Journal of Computational and Applied Mathematics
Solution of fuzzy integrated production and marketing planning based on extension principle
Computers and Industrial Engineering
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This paper revisits an efficient procedure for solving posynomial geometric programming (GP) problems, which was initially developed by Avriel et al. The procedure, which used the concept of condensation, was embedded within an algorithm for the more general (signomial) GP problem. It is shown here that a computationally equivalent dual-based algorithm may be independently derived based on some more recent work where the GP primal-dual pair was reformulated as a set of inexact linear programs. The constraint structure of the reformulation provides insight into why the algorithm is successful in avoiding all of the computational problems traditionally associated with dual-based algorithms. Test results indicate that the algorithm can be used to successfully solve large-scale geometric programming problems on a desktop computer.